ENHANCED MULTI-DISEASE RISK ASSESSMENT USING ENSEMBLE LEARNING ON STANDARDIZED UCI CLINICAL DATASETS

This paper presents an enhanced multi-disease risk assessment system that leverages ensemble learning techniques on standardized UCI clinical datasets to predict multiple diseases simultaneously.The system implements a novel approach combining XGBoost-based ensemble learning with individual disease-specific models to predict eight critical diseases: diabetes, heart disease, Parkinson's disease, lung cancer, breast cancer, chronic kidney disease, hepatitis, and liver disease.The implementation utilizes standardized clinical datasets from UCI Machine Learning Repository, ensuring consistency and reliability in predictions.The system features a comprehensive symptom-based prediction mechanism, where patient symptoms are processed through an ensemble of machine learning models to provide accurate disease predictions along with associated risk probabilities.The implementation includes a user-friendly web interface built with Streamlit, enabling realtime disease prediction and providing detailed disease descriptions and precautionary measures.The system's effectiveness is demonstrated through its ability to handle multiple disease predictions simultaneously while maintaining high accuracy across different disease categories.This research contributes to the field of healthcare analytics by providing a unified platform for multi-disease risk assessment, potentially aiding healthcare professionals in early disease detection and preventive care planning.